# .black[.large[.font100[.bold[Use of GIS in Public Health]]]] ## .black[.large[.bold[Charms & Chellenges from Experience]]] #### .black[.large[.bold[2023-06-03]]] .bottom[ .pull-right[ .black[.font150[.bold[Dr. Biju Soman, ]]] .black[.large[Professor & Head, AMCHSS]] <br/> .black[.font110[.code[`bijusoman@sctimst.ac.in`]]] ] ] <img src="assets/sctimst_logo_long.png" align="left" width="95%"/> --- class: slide-with-logo, middle # About SCTIMST, Trivandrum ### Institution of National Importance with the status of a University in 1980 under the Department of Science and Technology, Govt. of India by an Act of Parliament (Act 52 of 1980). ### The institute has the status of a university and offers excellent research and training facilities. --- class: middle .pull-left-1[ ### .bold[One of the unique institutions in the country.] ---- ### .bold[It has three wings] ---- ### .bold[Academics:] + Diploma programs + MD & MPH program + PhD program + Post Doctoral Fellowships <br/> ] .pull-right-2[ <img src="images/three_institutes_sctimst.jpg" align="left" width="95%"/> ] --- # AMCHSS, SCTIMST, Trivandrum <br/> ### Offers Masters level courses and Ph.D. - MPH and PhD also offered in collaboration with National Institute of Epidemiology, Chennai and Christian Medical College, Vellore. ### Regional Center for Health Technology Assessment - Established under the Department of Health Research (DHR), MoHFW, Govt of India. ### Advanced Center for Clinical Trials - Established under the Indian Council for Medical Research (ICMR) --- # Activities at AMCHSS - Research Collaborations with various National and International Universities, Public Health Agencies, and Governments - Community Participation - Empowering Tribal (ST) Communities .pull-left-2[  ] .pull-right-1[ - Some of the areas of work: + Non-Communicable Disease + Health Policy + Health Equity + Gender + Health Economics + Public Health Informatics ] --- # Field Practice Area: <br/> .bold[Athiyannoor Sree Chitra Action (ASA)] <br/> .pull-left-2[  ] .pull-right-1[ - Households Mapped: >35,000 - Population Covered: ~2 lakh - Across ~60 sq km - 6 Grama Panchayats - Participatory GIS ] <br/> .pull-right-2[ .small[.font50[ Support from: Center for Earth Sciences (CESS), Health Information System Project - India (HISP-India), University of Oslo, Norway, National Health Systems Resource Center (NHSRC), New Delhi, Capacity Development for Decentralization in Kerala (CapDecK) ]]] --- # Spatial Data Science at AMCHSS - Research Work + Rising Caesarean Sections in India: NFHS Data + Built environment and non-communicable diseases + Dengue in Punjab + Interstate Migration in Kerala + AMCHSS COVID-19 Dashboard - Capacity Building and Training Activities + Workshop Series + Government Consultations --- # Collaborations + `Edit-a-thon` - UNOOSA, University of Koblenz & Landau, Germany + Access to stroke facilities in India - University of Florida, USA - University of Chicago, USA - SVIN + Health Equity Research - University of Oslo, Norway + Outbreak and Pandemic Management: Course Development - DMgtAsia - DIKU & University of Oslo, Norway + Digital Health Policy: Course Development - DMgtAsia - DIKU & University of Oslo, Norway --- # Workshop Series <br/> .pull-left-2[ ### Five Cohorts: 2 in 2021, 3 in 2022 ### Over 120 Health Professionals ] .pull-right-1[ <img src="images/RIntro2022_hex.png" align="center" width="50%"/> <img src="images/R4SpatialEpi.png" align="center" width="50%"/> ] .pull-left-2[ - World Health Organization, India Office - Centre for Disease Control, US Embassy - National Institute of Epidemiology, Chennai - Armed Forces Medical College, Pune - All India Institute of Medical Sciences ] ### `https://amchss.github.io/RIntro2022/` --- class: sydney-blue, middle # .Large[Case Studies] --- class: middle # Rising Cesarean Sections in India - Many socio-demographic factors have been attributed to the rise in caesarean sections. But, systematic evidence is still lacking. - Publicly available data from large scale national surveys has the potential to offer much needed insights. - When combined with geo-spatial methodology has the potential to help uncover the hidden inequities in maternal healthcare. - Generate evidence and aid in informed decision-making in health policy. ### Objectives 1. To study the patterns of caesarean section at the state and district level. 2. To investigate spatial clustering of caesarean sections across districts. --- # Data Sources <br/> .pull-left-5[ ### The National Family Health Survey (NFHS) .small[.font70[ - NFHS-4 (2015-16) & NFHS-5 (2019-21) - District Level Fact Sheets - `*.pdf` format - `>`100 indicators on Maternal & Child Health ]]] .pull-right-5[ ### Health Management Information System (HMIS) .small[.font70[ - Longitudinal data (2011 - 2019) - Facility level data - Monthly reports - `*.xls` and `*.csv` ]]] <br/> <br/> Traditional approaches may not be ideal considering the volume and diversity of the data. .right[ ### `\(\longrightarrow\)` Data Science Approach ] --- ### Data Preparation .left-column[ ### Three Steps .small[ .font80[ 1. Web Scraping 2. Data Extraction 3. Preparation - Cleaning - Wrangling - Linkage <br/> - Reproducible - Open Source - Peer Reviewed ] ] ] .right-column[ <img src="images/schema.jpg" align="center" width="100%"/> ] <br/> <br/> .right[ ### .red[Cleaned] `\(\longrightarrow\)` .red[Exported] `\(\longrightarrow\)` .red[Exploratory Data Analysis] ] --- # Results ### Proportion of births by caesarean: <br/> 17.2% in 2015-16 `\(\longrightarrow\)` 21.5% in 2019-21. #### Higher increase in rural areas as compared to urban. #### More in private facilities as compared to public facilites. .center[ <img src="images/urban_rural_ridge_plot.png" align="center" width="90%"/> ] --- # Trends & Patterns of CS in India .left-column[ .font60[ ### Increasing Trends - National - State - District ### Varying patterns - Between States - Within States ] ] .right-column[ <img src="images/animation2.gif" align="center" width="85%"/> ] .right[.footnote[.red[.bold[Data Source:]] Based on HMIS data from 2011-2019. Available at .bold[`https://data.gov.in`]]] --- ## Spatial Analysis .pull-left-2[ <style type="text/css"> .tg {border-collapse:collapse;border-color:#93a1a1;border-spacing:0;} .tg td{background-color:#fdf6e3;border-bottom-width:1px;border-color:#93a1a1;border-style:solid;border-top-width:1px; border-width:0px;color:#002b36;font-family:Arial, sans-serif;font-size:14px;overflow:hidden;padding:10px 5px; word-break:normal;} .tg th{background-color:#657b83;border-bottom-width:1px;border-color:#93a1a1;border-style:solid;border-top-width:1px; border-width:0px;color:#fdf6e3;font-family:Arial, sans-serif;font-size:14px;font-weight:normal;overflow:hidden; padding:10px 5px;word-break:normal;} .tg .tg-cly1{text-align:left;vertical-align:middle;font-size:10px} .tg .tg-0lax{text-align:left;vertical-align:top} .tg .tg-d54t{background-color:#eee8d5;text-align:left;vertical-align:middle;font-size:10px} </style> <table class="tg"> <thead> <tr> <th class="tg-0lax">Parameter</th> <th class="tg-0lax">Facility</th> <th class="tg-0lax">NFHS</th> <th class="tg-0lax">G Statistic</th> <th class="tg-0lax">Z Score</th> <th class="tg-0lax">P Value</th> </tr> </thead> <tbody> <tr> <td class="tg-d54t" rowspan="4">Institutional Delivery</td> <td class="tg-d54t" rowspan="2">Overall</td> <td class="tg-d54t">NFHS-4</td> <td class="tg-d54t">0.00776</td> <td class="tg-d54t">6.80576</td> <td class="tg-d54t"><0.0001</td> </tr> <tr> <td class="tg-cly1">NFHS-5</td> <td class="tg-cly1">0.00762</td> <td class="tg-cly1">4.62108</td> <td class="tg-cly1"><0.0001</td> </tr> <tr> <td class="tg-d54t" rowspan="2">Public</td> <td class="tg-d54t">NFHS-4</td> <td class="tg-d54t">0.00799</td> <td class="tg-d54t">9.03936</td> <td class="tg-d54t"><0.0001</td> </tr> <tr> <td class="tg-cly1">NFHS-5</td> <td class="tg-cly1">0.00784</td> <td class="tg-cly1">7.76971</td> <td class="tg-cly1"><0.0001</td> </tr> <tr> <td class="tg-d54t" rowspan="6">Caesarean Sections</td> <td class="tg-d54t" rowspan="2">Overall</td> <td class="tg-d54t">NFHS-4</td> <td class="tg-d54t">0.01099</td> <td class="tg-d54t">18.67214<br></td> <td class="tg-d54t"><0.0001</td> </tr> <tr> <td class="tg-cly1">NFHS-5</td> <td class="tg-cly1">0.01081</td> <td class="tg-cly1">19.13736</td> <td class="tg-cly1"><0.0001</td> </tr> <tr> <td class="tg-d54t" rowspan="2">Public</td> <td class="tg-d54t">NFHS-4</td> <td class="tg-d54t">0.00810</td> <td class="tg-d54t">8.28497</td> <td class="tg-d54t"><0.0001</td> </tr> <tr> <td class="tg-cly1">NFHS-5</td> <td class="tg-cly1">0.00808</td> <td class="tg-cly1">9.00839</td> <td class="tg-cly1"><0.0001</td> </tr> <tr> <td class="tg-d54t" rowspan="2">Private<br></td> <td class="tg-d54t">NFHS-4</td> <td class="tg-d54t">0.01082</td> <td class="tg-d54t">19.18451</td> <td class="tg-d54t"><0.0001</td> </tr> <tr> <td class="tg-0lax">NFHS-5</td> <td class="tg-0lax">0.00749</td> <td class="tg-0lax">17.28355</td> <td class="tg-0lax"><0.0001</td> </tr> </tbody> </table> <br/><br/> .bold[Evidence of spatial clustering] Moran’s I (Global): p-value < 0.01 <br/> Getis-Ord General G (Local): p-value < 0.01 ] .pull-right-1[ <img src="images/ohsa_cs_overall_nfhs5.png" align="right" width="100%"/> Optimised Hot Spot Analysis <br> NFHS-5 (2019-21) ] --- ### .large[Hot Spot Analysis:] Public vs Private Facilities .pull-left-5[<img src="images/ohsa_cs_public_nfhs5.png" align="center" width="85%"/> Public Hospitals - NFHS-5 (2019-2021)] .pull-right-5[<img src="images/ohsa_cs_private_nfhs5.png" align="center" width="85%"/> Private Hospitals - NFHS-5 (2019-2021)] --- class: middle ### Trends across Time: HMIS Data (2011-2019) .pull-left-1[ .code40[ ````r plot_fn <- function(x){ cs_hotspot_analysis <- arc.open(filepath) cs_hotspot_analysis_data <- arc.select(cs_hotspot_analysis) cs_hotspot_analysis_sf <- arc.data2sf(cs_hotspot_analysis_data) cs_hotspot_analysis_sf %>% ggplot() + geom_sf(aes(fill = GiZScore, color = GiZScore)) + scale_fill_viridis_c() + scale_color_viridis_c() } plots <- 2011:2019 %>% map(plot_fn) ```` ] ] .pull-right-2[ <img src="images/cs_hotspots_2011_2019.jpg" align="center" width="80%"/> ] --- # Built Environment and Non-Communicable Disease <br/> <br/> ### Objectives 1. To study the distribution of built environment variables in Kerala 2. To find spatial clusters of diabetes and physical inactivity among sample population in Kerala and to evaluate built environment characteristics within clusters of high and low rates. 3. To determine the relationship between built environment variables in the neighborhood and prevalence of NCDs among sample population in Kerala --- <img src="images/joanna_1.jpg" align="center" width="70%"/> <img src="images/joanna_2.jpg" align="right" width="80%"/> --- ### Landsat-8 & SRTM .center[ <img src="images/joanna_3.jpg" align="center" width="90%"/> <img src="images/joanna_4.jpg" align="center" width="90%"/> ] --- # Identification of Diabetic clusters among urban study sites <br/> <img src="images/joanna_5.jpg" align="center" width="85%"/> --- # Studying Interstate in-migration towards Kerala .pull-left-1[ <img src="images/tijo_2.jpg" align="center" width="100%"/> ] .pull-right-2[ <img src="images/tijo_1.png" align="center" width="100%"/> ] .pull-right-2[ .small[.font50[ Census data on migration is compromised by the definitions used but still provides a comprehensive description of the volume and patterns of migration. These patterns provide much needed insights for designing and implementing migrant inclusive welfare policies at the state level. ]] ] --- # Modelling incidence of Dengue in Punjab .pull-left-1[ .small[ - Data Science Approach - RHIS Data - Reproducible Framework ] <img src="images/gurpreet.jpg" align="center" width="100%"/> ] .pull-right-2[ .center[ <img src="images/blocks_anim_SIR.gif" align="center" width="80%"/> ] ] --- # AMCHSS COVID-19 Dashboard <img src="images/ICMR_Proposal_Dashboard/1.JPG" align="center" width="100%"/> --- # AMCHSS COVID-19 Dashboard <img src="images/home.JPG" align="center" width="100%"/> --- # AMCHSS COVID-19 Dashboard .panelset[ .panel[.panel-name[Epicurves] .center[
] ] .panel[.panel-name[GR] .center[
] ] .panel[.panel-name[DT] .center[
] ] .panel[.panel-name[Rt] .center[
] ] .panel[.panel-name[SIR] .center[
] ] .panel[.panel-name[SMR] .center[
] ] .panel[.panel-name[Mobility] .center[ <img src="images/population_mobility.JPG" width="100%" /> ] ] ] --- # Semi-automated Report Generation .panelset[ .panel[.panel-name[Dynamic Reports] <style type="text/css"> .pull-left { float: left; width: 33%; } .pull-right { float: right; width: 67%; } .pull-right ~ p { clear: both; } </style> .pull-left[ ```r # Specify the State state = "Kerala" # Specify the District district = "Wayanad" ``` ] .pull-right[ <img src="images/ICMR_Proposal_Dashboard/12.JPG" width="100%" /> ] ] .panel[.panel-name[Comparison with other Districts] .center[ <img src="images/ICMR_Proposal_Dashboard/9.JPG" width="75%" /> ] ] .panel[.panel-name[Patterns] .center[ <img src="images/ICMR_Proposal_Dashboard/11.JPG" width="85%" /> ] ] ] --- # Mapping access to Stroke Centers in India .center[ <img src="images/data_sources.jpg" align="center" width="90%"/> ] --- # Distribution of Population Centers and Stroke Facilities in India <br/> .pull-left[ <img src="images/pop-centers-map.png" align="center" width="90%"/> .center[Population Centers] ] .pull-right[ <img src="images/stroke-centers-map.png" align="center" width="90%"/> .center[Stroke Facilities] ] --- # Access to Stroke Centers in India .center[ <img src="images/duration-map.png" align="center" width="90%"/> ] --- # Travel times to Stroke Centers by different regions <img src="images/zonewise-dist-dur-boxplot.png" align="center" width="90%"/> --- class: middle, center # Thank you! --- class: sydney-blue background-image: url(assets/sctimst_logo_white.png) background-size: 150px background-position: 5% 95% # .Large[Thanks!] .pull-right[.pull-down[ <a href="https://amchss-sctimst.shinyapps.io/covid_dashboard"> .white[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> amchss-sctimst.shinyapps.io/covid_dashboard] </a> <a href="https://amchss.github.io/RIntro2022"> .white[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;" xmlns="http://www.w3.org/2000/svg"> <path d="M326.612 185.391c59.747 59.809 58.927 155.698.36 214.59-.11.12-.24.25-.36.37l-67.2 67.2c-59.27 59.27-155.699 59.262-214.96 0-59.27-59.26-59.27-155.7 0-214.96l37.106-37.106c9.84-9.84 26.786-3.3 27.294 10.606.648 17.722 3.826 35.527 9.69 52.721 1.986 5.822.567 12.262-3.783 16.612l-13.087 13.087c-28.026 28.026-28.905 73.66-1.155 101.96 28.024 28.579 74.086 28.749 102.325.51l67.2-67.19c28.191-28.191 28.073-73.757 0-101.83-3.701-3.694-7.429-6.564-10.341-8.569a16.037 16.037 0 0 1-6.947-12.606c-.396-10.567 3.348-21.456 11.698-29.806l21.054-21.055c5.521-5.521 14.182-6.199 20.584-1.731a152.482 152.482 0 0 1 20.522 17.197zM467.547 44.449c-59.261-59.262-155.69-59.27-214.96 0l-67.2 67.2c-.12.12-.25.25-.36.37-58.566 58.892-59.387 154.781.36 214.59a152.454 152.454 0 0 0 20.521 17.196c6.402 4.468 15.064 3.789 20.584-1.731l21.054-21.055c8.35-8.35 12.094-19.239 11.698-29.806a16.037 16.037 0 0 0-6.947-12.606c-2.912-2.005-6.64-4.875-10.341-8.569-28.073-28.073-28.191-73.639 0-101.83l67.2-67.19c28.239-28.239 74.3-28.069 102.325.51 27.75 28.3 26.872 73.934-1.155 101.96l-13.087 13.087c-4.35 4.35-5.769 10.79-3.783 16.612 5.864 17.194 9.042 34.999 9.69 52.721.509 13.906 17.454 20.446 27.294 10.606l37.106-37.106c59.271-59.259 59.271-155.699.001-214.959z"></path></svg> amchss.github.io/RIntro2022] </a> <br><br><br> ]]